{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1b82cd31-6b07-4388-b7b2-a957e3f73836",
   "metadata": {},
   "source": [
    "### nltk安装"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "80f3e6f1-6103-444d-b8fd-71b1cc8a4104",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install nltk\n",
    "#码云上下载NLTK DATA, 下载压缩包解压，将packages文件夹下的文件复制到D://nltk_data\n",
    "#在使用过程如果报错，有可能是包里面的文件未解压而不能使用，解压即可"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32ac89ea-694a-41b6-971b-75812e0507d7",
   "metadata": {},
   "source": [
    "### 句子切分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1c3663c6-4c21-45fc-99ee-c4f77e504fe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#句子切分\n",
    "from nltk.tokenize import sent_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "19d00086-b8bc-4adf-8e53-3b5eb32bb835",
   "metadata": {},
   "outputs": [],
   "source": [
    "paragraph = 'You must follow me carefully. I shall have to controvert one or twoideas that are almost universally accepted. The geometry, forinstance, they taught you at school is founded on a misconception.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c81bd2ad-a1ed-4958-bc5d-61c10a03febd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['You must follow me carefully.',\n",
       " 'I shall have to controvert one or twoideas that are almost universally accepted.',\n",
       " 'The geometry, forinstance, they taught you at school is founded on a misconception.']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#句子切分\n",
    "tokenize_text = sent_tokenize(paragraph)\n",
    "tokenize_text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "903fb5e1-909e-4642-b1e0-12f04f1dcd54",
   "metadata": {},
   "source": [
    "### 分词"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "71c10f9f-23f8-4a76-b378-b7c82c31ee80",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import word_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b10b50ba-d0cc-41cf-8d5c-e7c44cba51e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['You', 'must', 'follow', 'me', 'carefully', '.']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = 'You must follow me carefully.'\n",
    "tokenized_text = word_tokenize(text)\n",
    "tokenized_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fac7e7cc-86f9-468d-b8c9-50584d2ac481",
   "metadata": {},
   "outputs": [],
   "source": [
    "#去除最后一个标点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3f76bd2b-76a3-4a74-9123-6c73e32f0bd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#方法一--python的string，将原句子的.改为了' '\n",
    "import string\n",
    "string.punctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "43122be6-ce5f-494b-87b5-563702169772",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_text = text.translate(str.maketrans(string.punctuation, ' ' * len(string.punctuation)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d9e04070-463f-4d9b-80d1-a705133b49db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['You', 'must', 'follow', 'me', 'carefully']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_text_1 = word_tokenize(new_text)\n",
    "tokenized_text_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cc703c0e-be96-412b-b428-17c064e2f4b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['!',\n",
       " '\"',\n",
       " '#',\n",
       " '$',\n",
       " '%',\n",
       " '&',\n",
       " \"'\",\n",
       " '(',\n",
       " ')',\n",
       " '*',\n",
       " '+',\n",
       " ',',\n",
       " '-',\n",
       " '.',\n",
       " '/',\n",
       " ':',\n",
       " ';',\n",
       " '<',\n",
       " '=',\n",
       " '>',\n",
       " '?',\n",
       " '@',\n",
       " '[',\n",
       " '\\\\',\n",
       " ']',\n",
       " '^',\n",
       " '_',\n",
       " '`',\n",
       " '{',\n",
       " '|',\n",
       " '}',\n",
       " '~']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 方法二--对tokenized_text过滤\n",
    "punctuation = list(string.punctuation)\n",
    "punctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "396d4ec1-8b4d-4b72-ab0e-c3147550040c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['You', 'must', 'follow', 'me', 'carefully']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words = [word for word in tokenized_text if word not in punctuation]\n",
    "words"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec0c83ca-7d49-4552-a5ad-bafa9cf34305",
   "metadata": {},
   "source": [
    "### 去除停用词"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "591f6aac-5460-4dca-af0e-563b81cb62f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "stop_words = stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4e73ec15-2cf4-4f7f-b209-f58564839058",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['You', 'must', 'follow', 'carefully']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_words = [word for word in words if word not in stop_words]\n",
    "new_words"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecc5c4f8-acdf-4321-9778-f8e8a486ee07",
   "metadata": {},
   "source": [
    "### 词频提取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "74f99f7a-dc4a-4463-a6cb-084c0d9310ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Samples', ylabel='Counts'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "word_freqs = nltk.FreqDist(w.lower() for w in new_words)\n",
    "#画出词频图\n",
    "word_freqs.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9f4c55bd-4b93-45b3-9dfc-08bec3dad3bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Samples', ylabel='Cumulative Counts'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 只出现频率最高的几个词. \n",
    "word_freqs.plot(3, cumulative=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d8dbd63f-b530-40fd-8515-1d935dcb72f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 单词搜索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98a9e307-01e3-4354-b67a-530046f41f47",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.book import *"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
